Maximizing traffic flow within an area, such as a city, so that stop-and-go traffic and traffic jams do not occur is a complex optimization problem, which thus far has not been efficiently solved with classical computers in short or even finite time. The problem at hand is NP-hard, as there is a combinatorial explosion in the solution space, which currently makes it impossible or near impossible for classical computers to calculate the optimal solution or a solution close to the optimum within seconds. For example, assuming 500 cars cause traffic flux minimization on a certain road or road segment, and 3 alternative routes per car are provided, then the solution space is of the size 3500.
Current systems focus on real-time analysis of traffic data and redistribute traffic without considering the traffic in the target areas. When congestion on route A occurs, vehicles are redistributed to route B, but may cause congestion there. More sophisticated models are based on a fluid dynamics-approach.
Therefore, there is a need for a traffic flux maximization system that does not result in a significant decrease flux in another area; continually or permanently optimizes traffic flux, ideally based on interval data transmitted from vehicles; and recognizes traffic minimization sufficiently prior in time before it happens.